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Backdoor Attack

Backdoor attacks inject maliciously constructed data into a training set so that, at test time, the trained model misclassifies inputs patched with a backdoor trigger as an adversarially-desired target class.

Papers

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Showing 511–520 of 523 papers

TitleStatusHype
BadNL: Backdoor Attacks against NLP Models with Semantic-preserving Improvements—0
Adversarial examples are useful too!Code0
Rethinking the Trigger of Backdoor Attack—0
Dynamic Backdoor Attacks Against Machine Learning Models—0
On Certifying Robustness against Backdoor Attacks via Randomized Smoothing—0
Defending against Backdoor Attack on Deep Neural Networks—0
Targeted Forgetting and False Memory Formation in Continual Learners through Adversarial Backdoor Attacks—0
NeuronInspect: Detecting Backdoors in Neural Networks via Output Explanations—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Defending Neural Backdoors via Generative Distribution ModelingCode0
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